Towards automating the Frenchay dysarthria assessment: Can neural phoneme posteriorgrams inform the analysis of dysarthric speech?

Dysarthria is a type of motor speech disorder that reflects abnormalities in motor movements required for speech production. In clinical practice, identifying characteristic signs and symptoms of the neuropathophysiology underlying a dysarthria is vital for diagnosis and management. The gold standard for dysarthria assessment is auditory-perceptual evaluation by a speech and language therapist for differential diagnosis and management decisions. As the process is time-consuming for clinicians, there is growing interest in automatic dysarthria assessment (ADA). Recent approaches to ADA primarily focus on the classification of broad intelligibility or speech severity labels. However, this does not have much clinical utility and the assessment of communication-relevant parameters do not distinguish between dysarthria types and pathomechanisms. Studies on the classification of dysarthria function or clinical test protocol scores focusing on aspects of dysarthric speech production (such as the Frenchay dysarthria assessment (FDA)) are limited. Therefore, this paper focuses on the preliminary steps towards clinically interpretable ADA, including automatic FDA assessment. The phoneme posteriorgram (PPG) is a time-varying categorical distribution over acoustic speech units, and recent work demonstrates interpretable speech pronunciation distance for downstream tasks, e.g. pronunciation reconstruction. This work extends recent advances in posterior-based phoneme research and mispronunciation models to dysarthria assessment, exploring the extent to which dysarthric speech features in the FDA (identified by auditory-perceptual evaluation in clinical practice) are captured by PPG information. To achieve this, FDA aspects are systematically evaluated. The results show that interpretable PPG probability can capture dysarthric speech features that are related to motor system dysfunction.